A Bionic Goal-Oriented Path Planning Method Based on an Experience Map
Brain-inspired bionic navigation is a groundbreaking technological approach that emulates the biological navigation systems found in mammalian brains. This innovative method leverages experiences within cognitive space to plan global paths to targets, showcasing remarkable autonomy and adaptability...
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MDPI AG
2025-05-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/5/305 |
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| author | Qiang Zou Yiwei Chen |
| author_facet | Qiang Zou Yiwei Chen |
| author_sort | Qiang Zou |
| collection | DOAJ |
| description | Brain-inspired bionic navigation is a groundbreaking technological approach that emulates the biological navigation systems found in mammalian brains. This innovative method leverages experiences within cognitive space to plan global paths to targets, showcasing remarkable autonomy and adaptability to various environments. This work introduces a novel bionic, goal-oriented path planning approach for mobile robots. First, an experience map is constructed using NeuroSLAM, a bio-inspired simultaneous localization and mapping method. Based on this experience map, a successor representation model is then developed through reinforcement learning, and a goal-oriented predictive map is formulated to address long-term reward estimation challenges. By integrating goal-oriented rewards, the proposed algorithm efficiently plans optimal global paths in complex environments for mobile robots. Our experimental validation demonstrates the method’s effectiveness in experience sequence prediction and goal-oriented global path planning. The comparative results highlight its superior performance over traditional Dijkstra’s algorithm, particularly in terms of adaptability to environmental changes and computational efficiency in optimal global path generation. |
| format | Article |
| id | doaj-art-cfdcc8ed7ab54bb78abb7d9e07625bee |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-cfdcc8ed7ab54bb78abb7d9e07625bee2025-08-20T02:33:43ZengMDPI AGBiomimetics2313-76732025-05-0110530510.3390/biomimetics10050305A Bionic Goal-Oriented Path Planning Method Based on an Experience MapQiang Zou0Yiwei Chen1Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, ChinaFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, ChinaBrain-inspired bionic navigation is a groundbreaking technological approach that emulates the biological navigation systems found in mammalian brains. This innovative method leverages experiences within cognitive space to plan global paths to targets, showcasing remarkable autonomy and adaptability to various environments. This work introduces a novel bionic, goal-oriented path planning approach for mobile robots. First, an experience map is constructed using NeuroSLAM, a bio-inspired simultaneous localization and mapping method. Based on this experience map, a successor representation model is then developed through reinforcement learning, and a goal-oriented predictive map is formulated to address long-term reward estimation challenges. By integrating goal-oriented rewards, the proposed algorithm efficiently plans optimal global paths in complex environments for mobile robots. Our experimental validation demonstrates the method’s effectiveness in experience sequence prediction and goal-oriented global path planning. The comparative results highlight its superior performance over traditional Dijkstra’s algorithm, particularly in terms of adaptability to environmental changes and computational efficiency in optimal global path generation.https://www.mdpi.com/2313-7673/10/5/305bionic goal-oriented path planningexperience mapsuccessor representation model |
| spellingShingle | Qiang Zou Yiwei Chen A Bionic Goal-Oriented Path Planning Method Based on an Experience Map Biomimetics bionic goal-oriented path planning experience map successor representation model |
| title | A Bionic Goal-Oriented Path Planning Method Based on an Experience Map |
| title_full | A Bionic Goal-Oriented Path Planning Method Based on an Experience Map |
| title_fullStr | A Bionic Goal-Oriented Path Planning Method Based on an Experience Map |
| title_full_unstemmed | A Bionic Goal-Oriented Path Planning Method Based on an Experience Map |
| title_short | A Bionic Goal-Oriented Path Planning Method Based on an Experience Map |
| title_sort | bionic goal oriented path planning method based on an experience map |
| topic | bionic goal-oriented path planning experience map successor representation model |
| url | https://www.mdpi.com/2313-7673/10/5/305 |
| work_keys_str_mv | AT qiangzou abionicgoalorientedpathplanningmethodbasedonanexperiencemap AT yiweichen abionicgoalorientedpathplanningmethodbasedonanexperiencemap AT qiangzou bionicgoalorientedpathplanningmethodbasedonanexperiencemap AT yiweichen bionicgoalorientedpathplanningmethodbasedonanexperiencemap |